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hyperopt.dev vs Tare

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

hyperopt.dev compared with Tare
Detailhyperopt.dev logo hyperopt.devTare logo Tare
What it doesAutoML that just works. Keep training local. See where your AI coding agent's tokens go, cut the waste without breaking the prompt cache, and get a receipt for every saving
StageMVP Not given
FoundedNot given Not given
Based inNot given Not given
Pricing modelFree trial Freemium
PricingFree beta with no billing. Paid plans launching in the future; pricing not yet announced. Join the waitlist for paid plans without payment or purchase commitment. Free for open source (Wrapped, all fixes with undo, receipt, skill, plugin, MCP server). Team: $30 per developer, or 10% of verified savings, whichever is lower (spend by engineer, cost per merged PR, CI gate). Enterprise: contact for self-hosted, SSO, finance exports, procurement-grade reports.
Key features
  • Python SDK for tuning suggestions
  • Local model training with remote suggestions
  • Supports classification and regression
  • Included CPU demo workloads
  • Efficient observation budget allocation
  • Model ranking and selection
  • Reference scheduler implementations
  • HTTPS secure communication
  • Session log analysis with token pricing breakdown
  • Categorized spending attribution by startup context, tool output, sub-agents, and cache rebuilds
  • Safe automatic fixes for output trimming and cache countdown
  • Undo capability for all changes
  • Receipt comparing cost-per-commit before and after with 90% confidence range
  • Shareable card with visualization of token usage
  • Command output trimming to reduce context bloat
  • Cache expiration countdown in status line
  • MCP server integration for any agent
  • Claude Code plugin with skill and MCP server
What makes it different
  • Trains locally, not on the service—your models and data stay on your computer
  • Efficient tuning using only half the observation budget of full training
  • Outperforms reference schedulers in benchmarks on diverse datasets
  • Free beta with no billing or account costs
  • Reads actual logs your agent already keeps, not a third-party service
  • Fixes only trim new content without rewriting cache to preserve prompt optimization
  • Open source under Apache 2.0 with no incentive to make you spend more tokens
Community votes00
DR (Domain Rating by Ahrefs)Not checked yet no change since last checkNot checked yet no change since last check
Trust Flow (Majestic)30
Citation Flow (Majestic)130
Referring domains (Majestic)20

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